{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sigmod 激活函数表达式\n",
    "# y = 1/(1+e^-x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "plt.figure(figsize=(15,8))\n",
    "#指定默认字体\n",
    "matplotlib.rcParams['font.sans-serif'] = ['SimHei']\n",
    "matplotlib.rcParams['font.family']='sans-serif'\n",
    "#解决负号'-'显示为方块的问题\n",
    "matplotlib.rcParams['axes.unicode_minus'] = False\n",
    "ax = plt.gca() # get current axis 获得坐标轴对象\n",
    "ax.spines['right'].set_color('none') \n",
    "ax.spines['top'].set_color('none') # 将右边上边的两条边颜色设置为空 其实就相当于抹掉这两条边\n",
    "ax.xaxis.set_ticks_position('bottom')   \n",
    "ax.yaxis.set_ticks_position('left')# 指定下边的边作为 x 轴 指定左边的边为 y 轴\n",
    "ax.spines['bottom'].set_position(('data', 0))  #指定data 设置的bottom(也就是指定的x轴)绑定到y轴的0这个点上\n",
    "ax.spines['left'].set_position(('data', 0))\n",
    "x = torch.linspace(-10,10,5000)\n",
    "xx = 1/(1+math.e**(-x))\n",
    "#函数图像\n",
    "l1,=plt.plot(x.numpy(),xx.numpy())\n",
    "#导数图像\n",
    "l2,=plt.plot(x.numpy(),(xx*(1-xx)).numpy(),\"--\")\n",
    "plt.legend(handles=[l1,l2],labels = [\"函数图像\",\"导数图像\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([-10.0000,  -9.9960,  -9.9920,  ...,   9.9920,   9.9960,  10.0000])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-1.,  0.,  1.])\n",
      "tensor([0.2689, 0.5000, 0.7311])\n",
      "tensor([0.2689, 0.5000, 0.7311])\n"
     ]
    }
   ],
   "source": [
    "a = torch.Tensor([-1,0,1])\n",
    "print(a)\n",
    "print(torch.sigmoid(a))\n",
    "print(a.sigmoid())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.2689, 0.5000, 0.7311])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.sigmoid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
